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\def\TITLE{Parsing Morphologically-Rich Languages}
\def\AUTHOR{Reut Tsarfaty}
\def\AFFILIATION{Weizmann Institute of Science}
\def\LECTURE{\ \#1}
\def\lcSYNTHESIS{Synthesis Lectures on Language Technology}
\def\SYNTHESIS{\uppercase{Synthesis Lectures on Language Technology}}

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\ABSTRACT
\noindent
%Parsing Morphologically-Rich Languages
%\noindent
%A parsing  system automatically predicts, for an input sentence in a human language, a syntactic structure that reflects its human perceived interpretation. Parsers are  key components  in a range of technological applications,  from question answering systems to machine translation, and the predicted structures are essential for further semantic and pragmatic processing. The best parsing systems to-date are data-driven and statistical, and they were shown to  parse  English texts in  high accuracy. Cross-linguistic evaluation campaigns reveal that  many of these systems do not perform as well when applied to  other types of languages, and in particular to 

Statistical  methods for broad-coverage  parsing, constituency-based and dependency-based alike,  have seen  great successes in recent years, and were shown to obtain high accuracy in parsing English. At the same time, empirical studies have repeatedly shown that these  methods   under-perform when they are applied to languages of a different type, and in particular, to languages  known as morphologically rich languages (MRLs). 

The complex internal structure of words in MRLs and the variability in their word ordering patterns  cast doubt on the  applicability of existing parsing models for MRLs thus establishing  a poor starting point for   MRL language technology (Question Answering, Information Extraction, Machine Translation, and so on). Parsing MRLs has thus become a new focal point and a vibrant research area in  computational linguistics and natural language processing, as is evident by  recent  workshops, a  journal special issue and  a  dedicated shared task.
%This book introduces parsing MRLs (henceforth, PMRL) by exploring the intricate relationships between refined linguistic structure of MRLs and advanced structure prediction that have been developed for accurate statistical parsing.

This book introduces statistical models that can effectively cope with parsing MRLs. We present   constituency-based and dependency-based methods, as well as novel extensions designed specifically for the task. The book covers representation types, learning algorithms, decoding algorithms and evaluation methods, and provides a principled treatment of the application of general-purpose structure prediction and machine learning techniques for predicting the intricate linguistic structures that characterize MRLs. 

%This book introduces  statistical models that can effectively cope with parsing MRLs. We present extensions of well-studied constituency-based and dependency-based methods, as well as novel extensions designed specifically for the task. The book covers  representation types, learning algorithms, decoding algorithms and evaluation methods, and provides principled solution to the application of general-purpose structure prediction and machine learning algorithms  for predicting  the intricate linguistic structures that characterize MRLs. %The presentation of the main challenges and solutions in parsing MRLs is accommodated with faithful methods for comparative  of morpho-syntactic parsing in realistic and cross-framework scenarios.

%end ABSTRACT

\keywords{%
%Statistical Parsing, Structure Prediction,  Morphologically Rich Language, Phrase-Structure parsing, Dependency Parsing, Relational Networks,  Joint Modeling, Dynamic Programming, Evaluation Metrics , Universal  Schemes.
  Statistical Parsing, Structure Prediction,  Morphosyntax,  %Disambiguation, Dynamic Programming, 
  Evaluation Metrics, Universal Schemes.
}

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